Optimizing Forecast Precision for Iraq’s Imports with Hybrid Model

This paper presents an innovative approach to economic forecasting by integrating Artificial Neural Networks (ANN) and Wavelet Analysis (WANN) into a hybrid model, aimed at enhancing the prediction accuracy of Iraq’s import data from 1999 to 2020. The necessity for accurate forecasting methodologies is underscored by the volatile nature of global markets and the complex interdependencies within the global economic system. The proposed hybrid model leverages the pattern recognition capabilities of ANNs and the multi-resolution analysis of WANN to address the challenges of non-stationary economic data and to improve the extraction of meaningful insights from noisy datasets. The study employs a comparative analysis methodology, evaluating the performance of the hybrid ANN-WANN model against traditional ANN models using metrics such as Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). The results indicate a significant enhancement in forecasting accuracy with the hybrid model, suggesting its potential as a superior tool for economic analysis and policymaking. Furthermore, the paper explores the computational challenges and data preprocessing requirements associated with the deployment of hybrid models. It discusses the implications of the findings for economic analysts, policymakers, and business leaders, highlighting the benefits of integrating advanced computational techniques in economic forecasting. The research contributes to the theoretical and practical understanding of economic forecasting methodologies, offering a novel perspective on leveraging machine learning and signal processing technologies to tackle the intricacies of economic data analysis. This exploration opens new avenues for the development of more accurate, reliable, and timely forecasting models, which are crucial for navigating the uncertainties of the global economic landscape.